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Found 25 papers, 16 papers with code
SnapKV: LLM Knows What You are Looking for Before Generation
Specifically, SnapKV achieves a consistent decoding speed with a 3. 6x increase in generation speed and an 8. 2x enhancement in memory efficiency compared to baseline when processing inputs of 16K tokens.
Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads
We present two levels of fine-tuning procedures for Medusa to meet the needs of different use cases: Medusa-1: Medusa is directly fine-tuned on top of a frozen backbone LLM, enabling lossless inference acceleration.
Extensible and Efficient Proxy for Neural Architecture Search
We further propose a Discrete Proxy Search (DPS) method to find the optimized training settings for Eproxy with only a handful of benchmarked architectures on the target tasks.
Decouple Before Interact: Multi-Modal Prompt Learning for Continual Visual Question Answering
Based on our formulation, we further propose MulTi-Modal PRompt LearnIng with DecouPLing bEfore InTeraction (TRIPLET), a novel approach that builds on a pre-trained vision-language model and consists of decoupled prompts and prompt interaction strategies to capture the complex interactions between modalities.
Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models
Conditioned diffusion models have demonstrated state-of-the-art text-to-image synthesis capacity.
Ranked #1 on
Story Visualization
on Pororo
Extensible Proxy for Efficient NAS
(2) Efficient proxies are not extensible to multi-modality downstream tasks.
What Makes Convolutional Models Great on Long Sequence Modeling?
We focus on the structure of the convolution kernel and identify two critical but intuitive principles enjoyed by S4 that are sufficient to make up an effective global convolutional model: 1) The parameterization of the convolutional kernel needs to be efficient in the sense that the number of parameters should scale sub-linearly with sequence length.
Ranked #6 on
Long-range modeling
on LRA
Compilation and Optimizations for Efficient Machine Learning on Embedded Systems
Deep Neural Networks (DNNs) have achieved great success in a variety of machine learning (ML) applications, delivering high-quality inferencing solutions in computer vision, natural language processing, and virtual reality, etc.
Generic Neural Architecture Search via Regression
Such a self-supervised regression task can effectively evaluate the intrinsic power of an architecture to capture and transform the input signal patterns, and allow more sufficient usage of training samples.
Ranked #1 on
Neural Architecture Search
on NAS-Bench-101
(Spearman Correlation metric)
InfoBehavior: Self-supervised Representation Learning for Ultra-long Behavior Sequence via Hierarchical Grouping
Typically, the risk can be identified by jointly considering product content (e. g., title and image) and seller behavior.
TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data
Among ubiquitous multimodal data in the real world, text is the modality generated by human, while image reflects the physical world honestly.
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search Space
This raises the question of whether we can find an effective proxy search space (PS) that is only a small subset of GS to dramatically improve RandomNAS’s search efficiency while at the same time keeping a good correlation for the top-performing architectures.
Effective Algorithm-Accelerator Co-design for AI Solutions on Edge Devices
High quality AI solutions require joint optimization of AI algorithms, such as deep neural networks (DNNs), and their hardware accelerators.
GAP++: Learning to generate target-conditioned adversarial examples
Different from previous single-target attack models, our model can conduct target-conditioned attacks by learning the relations of attack target and the semantics in image.
EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions
We formulate the co-search problem by fusing DNN search variables and hardware implementation variables into one solution space, and maximize both algorithm accuracy and hardware implementation quality.
Self-supervised Adversarial Training
Recent work has demonstrated that neural networks are vulnerable to adversarial examples.
Learning To Characterize Adversarial Subspaces
To detect these adversarial examples, previous methods use artificially designed metrics to characterize the properties of \textit{adversarial subspaces} where adversarial examples lie.
Self-Supervised Learning For Few-Shot Image Classification
In this paper, we proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream tasks by learning from the data itself.
Ranked #4 on
Few-Shot Image Classification
on Mini-ImageNet - 1-Shot Learning
(using extra training data)
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems
Object detection and tracking are challenging tasks for resource-constrained embedded systems.
SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection
Developing artificial intelligence (AI) at the edge is always challenging, since edge devices have limited computation capability and memory resources but need to meet demanding requirements, such as real-time processing, high throughput performance, and high inference accuracy.
A Bi-Directional Co-Design Approach to Enable Deep Learning on IoT Devices
Developing deep learning models for resource-constrained Internet-of-Things (IoT) devices is challenging, as it is difficult to achieve both good quality of results (QoR), such as DNN model inference accuracy, and quality of service (QoS), such as inference latency, throughput, and power consumption.
FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge
While embedded FPGAs are attractive platforms for DNN acceleration on edge-devices due to their low latency and high energy efficiency, the scarcity of resources of edge-scale FPGA devices also makes it challenging for DNN deployment.
Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks
The task of Language-Based Image Editing (LBIE) aims at generating a target image by editing the source image based on the given language description.
SiamVGG: Visual Tracking using Deeper Siamese Networks
It combines a Convolutional Neural Network (CNN) backbone and a cross-correlation operator, and takes advantage of the features from exemplary images for more accurate object tracking.
Ranked #1 on
Visual Object Tracking
on OTB-50
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
We demonstrate CSRNet on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the WorldEXPO'10 dataset, and the UCSD dataset) and we deliver the state-of-the-art performance.
Ranked #3 on
Crowd Counting
on Venice
